10C-i APPENDIX 10C

. REBOUND RATE

TABLE OF CO NTENTS

10C.1 BACKGROUND ...................................................................................................10C-1

10C.2 OPERATING HOURS OF EFFICIENT AND STANDARD RESIDENTIAL

LIGHTING IN CALIFORN IA ..............................................................................10C-3

10C.3 NATIONAL LIGHTING DENSITY AS THE DETERMINANT OF

LIGHTING REBOUND ........................................................................................10C-3

10C.4 CONCLUSIONS

................................ ................................ ....................................10C-5

LIST OF TABLES

Table 10C.2.1 Sample Disposition of Residential Lighting Studies

................................ . 10C-3

Table 10C.3.1 U.S. Residential and Commercial Lighting in 2001 and 2010 ..................10C-4

Table 10C.3.2 Changes in Lamp Operating Hours Compared to Changes in Lighting Density, U.S. Residential and Commer cial Sectors ...................................10C-5

Table 10C.3.3 Estimate rebound rates based on LMC Data ..............................................10C-510C-1 10C.1 BACKGROUND

The U.S. Department of Energy ( DOE ) accounts for the direct rebound effect in its estimates of national energy savings. a Direct rebound is the concept that an increase in the

energy efficiency of equipment may induce indirectly greater usage by the end

- user , thereby undercutting expected energy savings . Economic theory suggests that if efficiency improvements decrease the cost of obtaining a desired service —lighting, in this case —then demand for that

service will increase , assuming demand is not saturated.

In the case of lighting, the incre ased usage should be normalized to floor area. Therefore, if a rebound is occurring there must be a change in lighting density (lumens per square foot). Any combination of the following potential consumer responses to increased lighting efficiency could increase lighting density and

, therefore

, indicate a positive rebound effect :

• an increase in the average operating hours of lamps,

• a tendency to replace less efficient lamps with more lumenous efficient lamp s, and

• a n increase in the number of fixtures or sockets per unit floor area (ft 2 ).

While it i s the change in service demand ( lighting densi ty in lm/ft 2 ), concomitant with an improvement in lighting efficiency, that is the final determinant of rebound, lacking such data,

rebound researchers hav e relied on the individual factors listed previously as proxies for lighting rebound. ( See

, for example , the papers by Nadel (1993) 1 and Greening et al. (2000), 2 which were the origin of the rebound rates proposed by DOE in the f ramework document for this

rulem aking.) In keeping with the literature on rebound, causation is not addressed in this analysis.

Lighting r ebound can be quantified by the following formula:

Observed Savings = Expected Savings × ( 1 − rr ) Eq. 10C.1

Where :

Observed Savings = observed reduction in power consumption per square foot (W/ft 2 ) during a period,

Expected Savings = expected reduction in power consumption per square foot (W/ft 2 ), given the efficiency improvements that occurred during that period, if lighting density (lm/ft 2 ) had remained constant, and

rr

= rebound rate.

a DOE does not include rebound effects in its NPV calculation, however, for the following reasons: The take

- back in energy consumption associated with the rebound effect provides consumers with increased value (e.g., increased lighting services). DOE believ es that, if it were able to monetize the increased value to consumers of the rebound effect, this value would be similar in value to the foregone energy savings. Therefore, the economic impacts on

consumers with or without the rebound effect are the same, so DOE does not adjust operating cost savings in the

NIA

.10C-2 Therefore, a direct rebound rate of 10 percent means that 10 percent of the energy savings expected to materia lize from efficiency improvements alone would not materialize because of

increased lighting density

.

During the f ramework p ublic m eeting

, the Edison Energy Institute (EEI) and the Northwest Energy Efficiency Alliance (NEEA) both commented that the rebound rates proposed by DOE for residential and commercial lighting appear too high (EEI, Public Meeting Transcript, No. 10 at p. 241; NEEA, Public Meeting Transcript, No. 10 at p. 240). In the

f ramework document, DOE proposed an 8.5 percent reboun d rate for re sidential lighting, based on the average of two hours-of-use studies cited by Greening et al. (2000) and a one percent rebound rate for commercial and industrial lighting, also derived from Greening et al. (2000).

In response to public comments , DOE conducted further research on rebound rates, starting with a review of the source materials quoted by Greening et a l. (2000) . The lighting rebound estimates quoted in t he Greening paper were taken from Nadel (1 993). Nadel , in turn, cited earlier surveys of lamp operating hours conducted by Pacific Gas & Electric Company ( PG&E ), Boston Edison (BE), and Ne w England Electric System (NEES). Greening et al. used the results of quoted PG&E and BE studies only as the b asis of their estimated 5 – 12 percent

rebound for residential lighting . The results of the two NEES studies were not used, apparently because they had conflicting results. This may have been the genesis of the conclusion by Greening et al. that the data available on lighting rebound were ‘inconclusiv e’. b

Given that th e source data for the proposed rebound rates are now more than two decades

old and were based on a single indicator of rebound (hours of use), which can be offset by other factors, DOE sought more recent evidence. Many authors have noted that appliance saturation

effects can reduce rebound

. 3

, 4

, 5

, 6

, 7 T herefore , rebound rates can change significantly with time

. 4
While Greening et al . (2000), and to a lesser extent Nadel (1993), has been repeatedly cited as a

source for lighting rebound rates, 5 , 6 , 8

, 9 DOE found no studies that evaluated rebound rates based

on more recent data. The National Energy Modeling System (NEMS) also cites Greening et al. 2

as the source of its rebound rates, but NEMS uses a comb ined average rebound rate of 15 percent for all appliances and applies that to lighting

, as well as to other technologies. The NEMS

ass umptions for commercial rebound are described in the Energy Information Administration’s

Commercial Sector Demand Module documentation for NEMS 10 , the rebound rate used in the

residential demand module was inferred

.

Lacking m ore recent journal publications on U.S. lighting rebound, DOE sought lighting

data from which rebound rates could be estimated. DOE identified two large studies of lamp operating hours in California that are directly comparable to the original studies cited by Nadel (1993) and Greenin g et al. (2000). DOE also used lighting data from its 2001 and 2010 U.S. Lighting M arket C haracterization (LMC) studies, which are a better source for national rebound rates, given the ir scope. 11

, 12 The results are described in sections 10C.2 and 10C.3

.

b See Table 3 footnote in Greening et al. (2000).10C-3 10C.2 OPERATING HOURS OF EFFICIENT AND STANDAR D RESIDENTIAL

LIGHTING IN CALIFORN IA

A 2005 study by KEMA conducted for three California utilities (PG&E, Southern California Edison Company, and San Diego Gas and Electric Company) compared hours of use of compact fluorescent lamps (CFL) 13 to hours of use of all res idential lamp types that were reported in a 1999 California Energy Commission study for the same service territory. 14 These

studies were based on large and more recent data sets for service areas that overlapped those reported in Nadel (1993) and Greening et al. and used the same metric . The sample sizes used in the two studies and the reported hours of lamp use are reported in Table 10C.2.1 . The studies found no discernible difference in lamps hours of use. Given that changes in lamp operating

hours are only one possible cause of rebound, and the data are only for California, DOE sought

national data to check the result . These are reported in the following section.

Table 10C.2.1 Sample Disposition of Residential Lighting Studies

KEMA 2005 13 CEC 1999 14

Residences 369 683

Fixtures 752 16,275 Lamps 1

, 514 26,203 *

CFLs only All lamps Reported Hours of Use of Lamps, Average Daily 2.3 hrs/day 2.34 hrs/day

* Total number of lamps calculated from reported average lamp per fixture values.

10C.3 NATIONAL LIGHTING DENSITY AS THE DETERMINANT OF

LIGHTING REBOUND

DOE sought evidence of U.S. lighting rebound based on national lighting data reported in

DOE ’ s 2001 and 2010 U.S. LMC studies. 11

, 12 As indicated in section 10C.1 , by definition a positive lighting rebound occurs if there is an increase in lighting density (lm/ft 2 ) concomitant with an increase in lighting efficiency

. A decrease in lighting density yiel ds a negative rebound rate, indicating a greater energy savings than expected from efficiency improvements alone.

DOE used data from the two LMC reports to estimate lighting density in 2001 and 2010 using the following equation:

Lighting Density ( lm / ft 2 ) = N lamps ∗ W lamp ∗ ε lamp Floorspace Eq. 10C.2

Where:

N l amps = the number of lamps in the category under consideration in the U nited States,

W lamp = the average wattage (W) of the lamp category

,10C-4 ε lamp = the average efficacy (lm/W) of the lamp category, and Floorspace = the total floor space in the sector ( ft 2 ).

As shown in Table 10C.3.1

, while reported lighting efficie ncy inc reased from 2001 to 2010, lighting density decreased, implying a negative rebound rate. The table reports the associated percentage decrease s in power consumption per lumen: 5 percent i n the residential sector and 21 percent in the commercial sector. In the absence of rebound, lighting power density (W/ft 2 ) would be expected to decrease by the same amount s. Instead, it drops by considerably larger percentages: 29 and 31 percent in the residential and comme rcial sectors, respectively.

This implies a large reduction in lighting demand : from 30 to 23 lumens per square foot in the

residential sector , and from 90 to 75 lumens per square foot in the commercial sector.

Table 10C.3.1 U.S. Residentia l and Commercial Lighting in 2001 and 2010Average Efficacy1Elamp(lm/W)Change inWattsper LumenLamps1(Millions)Average LampPower1(W)Floor Space1(Billion ft2)Average LightingDensity (lm/ft2)Lighting PowerDensity (W/ft2)Change in LightingPowerDensity (W/ft2)2001201020012010200120102001201020012010200120102010-EXP2

Residential 18 19

- 5% 4611 5812 63 46 174 223 30 23 1.7 1.2 1. 6

- 29%

Commercial 55 70

- 21% 1966 2069 56 42 67 81 90 75 1.6 1.1 1. 3

- 31%

1 Data taken from LMC reports . A ll other values calculated.

2 The expected power density in 2010 in the absence of rebound.

According to the LMC data, only in the residential sector are the reductions in lighting density explained (at least in part) by red uctions in operating hours. As shown in Table 10C.3.2

, in the residential sector lamp operating hours were estimated to have decreased by 10 percent, explainin g on ly part of the estimated 23 percent reduction in lighting density. In the commercial sector the reported lamp operating hours increased . However, LMC 2010 indicates that the large change in estimated operating hours could be an artifact of changes in the c haracterization of commercial versus industrial lighting. Therefore , the results should not be considered conclusive.10C-5 Table 10C.3.2 Changes in Lamp Operating Hours Compared to Changes in Lighting Density, U.S. Residential and Commercial Sectors

Sector and Lamp Type Average Daily Operating Hours 1 Percent Change in Operating

Hour s Average Lighting Density (lm/ft 2 ) Percent Change in Average Lighting Density

2001 2010 2001 2010

Residential (Total) 2 1.8

- 10% 30 23

- 23%

Commercial (Total) 9.9 11.2 13% 90 75

- 17%

1 Data taken from LMC reports. All other values calculated.

Using the result from Table 10C.3.2, DOE estimated the rebound rates implied by the

LMC data as follows:

rr = 1 − observed power savings

expected power savings Eq. 10C.3

Where :

observed power savings = the difference between the 2001 and 2010 lighting power density, and expected power savings = percentage change in watts per lumen multiplied by the 2001 lighting power density.

The results are reported in Table 10C.3.3

.

Table 10C.3.3 Estimate rebound rates based on LMC Data

Observed Power Savings Expected Power Savings Estimated R ebou n d Rate

Residential 0.5 0.09

- 4.9 (

- 490%)

Commercial 0.5 0.34

- 0.5 (

- 50%)

10C.4 CONCLUSIONS

DOE concludes that the most recent available data do not support a lighting rebound effect for either the residential or commercial sector. The data may indicate a systematic trend in

residential sector rebound, moving from a modest positive rebound in the early 1990s, to zero rebound in 2005, to a very large negative rebound in 2012 . An initial reduction in rebound rates from positive to zero is what is expected if demand for a service saturates. Goldstein Error! Bookmark

not defined. explains that negative rebounds can occur if consumers use efficiency cost savings to invest in more energy efficiency rather than in more energy consumption. The same may be true of commercial lighting, though there are fewer data to support such a conclusion. However,10C-6 given the uncertainties inherent in comparing different data sources for different times, DOE

took a conservative approach in estimating energy savings and assumed in this preliminary analysis no rebound effect for its reference scenario for both sectors. DOE also conducted a

sen sitivity analysis to determine the impact of the change in the assumed rebound rates compared to what had been proposed in the framework document and what has been used in NEMS. The results are described in chapter 10.

REFERENC ES

1 Nadel, S.M. The Takeback Effect: Fact or Fiction? 1993. ACEEE Publication, American Council for an Energy-Efficient Economy. Washington, D.C. Av ailable online at

< http://www.aceee.org/research -report/u933 >.

2 Greening, L.A., D.L. Greene, and C. Difiglio. Energy Efficiency and Consumption— the rebound effect—a survey. Energy Policy

. 2000. 28: pp. 389–401.

3 Gottron, F. Energy Efficiency and the Rebound Effect. 2001. Congressional Research Service.

4 Goldstein, D.B, Martinez, S., and Roy, R. 2011 Are There Rebound Effects from Energy Efficiency? - An Analysis of Empirical Data, Internal Consistency and Solutions.

< Electricitypolicy.com > (Last acce ssed December 11. 2012).

5 Sorrell, S. Jevons’ Parodox revisited: The Evidence for backfire from improved energy efficiency. Energy Policy . 2009. 37: pp. 1456-1469.

6 Geller, H. and Attali, A. The E xperience with Energy Efficiency Policies and Programmes in IEA Countries-Learning from the Critics. 2005. International Energy Agency.

7 Nadel, S. The Rebound Effect: Large or Small? 2012. ACEEE White Paper. American Council for an Energy-Efficient Economy. Washington D.C. Available online at:

< aceee .org/files/pdf/white -paper/rebound- large

- and -small.pdf >

8 Herring, H. and Roy, R. Technological innovation, energy efficient design and the rebound

effect. Technovation. 2007. 27: pp. 194-203.

9 Madlener, R. and Alcott, B. Energy rebound and economic growth: A review of the main issues

and research needs. Energy. 2009. 34: pp. 370-376.

10 U.S. Department of Energy—Energy Information Administration. Office of Integrated Analysis and Forecasting, Commercial Sector Demand Module of the National Energy Mode ling System: Model Documentation 2010, p. 57. May 2010. Washington, D.C. p. 57.

11 Nav igant Consulting Inc. 2001 U.S. Lighting Market Characterization . Department of Energy, Solid State Lighting Program. September, 2002.

< http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/lmc_vol1_final.pdf >10C-7 12 Nav igant Consulting Inc. 2010 U.S. Lighting Market Characterization . Department of Energy, Solid State Lighting Program. January, 2012. Available online at:

< http://www1.eere.energy.gov/buildings/ssl/tech_reports.html >

13 KEMA Inc., CFL Metering Study: Final Report , Prepared for Pacific Gas & Electric Company, San Francisco, California, San Diego Gas & Electric Company, San Diego, California, and Southern California Edison Company, Rosemead, California. Prepared by KEMA Inc., Oakland, California, February 25, 2005, page 4-1, 4-2.

14 Heschong Mahone Group . California Baseline Lighting Efficiency Technology Report

, Prepared for California Energy Commission. Prepared by Heschong Mahone Group., Fair Oaks, California, May 30, 1997.